Avoiding the pitfalls of gene set enrichment analysis with SetRank

BMC Bioinformatics

Methodology Article

Rémy Bruggmann¹  Cedric Simillion²  Heidi E.L. Lischer³  Robin Liechti⁴  Vassilios Ioannidis⁵ 

[1] Interfaculty Bioinformatics Unit and SIB Swiss Institute of Bioinformatics, University of Bern, Baltzerstrasse 6, 3012, Berne, Switzerland;Interfaculty Bioinformatics Unit and SIB Swiss Institute of Bioinformatics, University of Bern, Baltzerstrasse 6, 3012, Berne, Switzerland;Department of Clinical Research, University of Bern, Murtenstrasse 35, 3008, Berne, Switzerland;Interfaculty Bioinformatics Unit and SIB Swiss Institute of Bioinformatics, University of Bern, Baltzerstrasse 6, 3012, Berne, Switzerland;Present Address: URPP Evolution in Action; Institute of Evolutionary Biology and Environmental Studies (IEU), University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland;Vital-IT, SIB Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015, Lausanne, Switzerland;Vital-IT, SIB Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015, Lausanne, Switzerland;SIB Technology, SIB Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015, Lausanne, Switzerland;

关键词: GSEA;    Gene set enrichment analysis;    Pathway analysis;    Sample source bias;    Functional genomics;    Algorithm;    R package;    Web interface;   

DOI  :  10.1186/s12859-017-1571-6

 received in 2016-09-08, accepted in 2017-02-24,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundThe purpose of gene set enrichment analysis (GSEA) is to find general trends in the huge lists of genes or proteins generated by many functional genomics techniques and bioinformatics analyses.ResultsHere we present SetRank, an advanced GSEA algorithm which is able to eliminate many false positive hits. The key principle of the algorithm is that it discards gene sets that have initially been flagged as significant, if their significance is only due to the overlap with another gene set. The algorithm is explained in detail and its performance is compared to that of other methods using objective benchmarking criteria. Furthermore, we explore how sample source bias can affect the results of a GSEA analysis.ConclusionsThe benchmarking results show that SetRank is a highly specific tool for GSEA. Furthermore, we show that the reliability of results can be improved by taking sample source bias into account. SetRank is available as an R package and through an online web interface.

【 授权许可】

CC BY   
© The Author(s). 2017